Substantial contribution of transported emissions to organic aerosol in Beijing

Haze in Beijing is linked to atmospherically formed secondary organic aerosol, which has been shown to be particularly harmful to human health. However, the sources and formation pathways of these secondary aerosols remain largely unknown, hindering effective pollution mitigation. Here we have quantified the sources of organic aerosol via direct near-molecular observations in central Beijing. In winter, organic aerosol pollution arises mainly from fresh solid-fuel emissions and secondary organic aerosols originating from both solid-fuel combustion and aqueous processes, probably involving multiphase chemistry with aromatic compounds. The most severe haze is linked to secondary organic aerosols originating from solid-fuel combustion, transported from the Beijing–Tianjing–Hebei Plain and rural mountainous areas west of Beijing. In summer, the increased fraction of secondary organic aerosol is dominated by aromatic emissions from the Xi’an–Shanghai–Beijing region, while the contribution of biogenic emissions remains relatively small. Overall, we identify the main sources of secondary organic aerosol affecting Beijing, which clearly extend beyond the local emissions in Beijing. Our results suggest that targeting key organic precursor emission sectors regionally may be needed to effectively mitigate organic aerosol pollution.

The average for the whole measurement period is shown.High values over specific areas suggest strong precursor emissions in these areas (or within the typical transport route passing over this area).The middle of the colorbar shows the mean value of sulfate over the whole observation period and the axis extends to 5 times the mean and 0.2 times the mean in logarithmic scale (left panel).Annual emissions of SO2 (precursor of sulfate) for the year 2010 from the MIX emission inventory 1 .Over most regions, the estimated source areas of the emissions match well with the emission inventory, suggesting robust performance of the approach.However, contributions from some regions e.g.those south of the Mongolian border in the west, are likely overestimated due to air masses arriving from these regions consistently passing over high emission closer to the measurement station.

Figure SI 2 :
Figure SI 2: Levoglucosan concentration -atmospheric temperature relation: For 02.2018 -03.2019 levoglucosan concentrations were determined based on offline filter analysis using highperformance liquid chromatography with the pulsed amperometric detector method.For 11.2019 -06.2020 instead the quantified C6H10O5 signal (detected as C6H10O5I -) from in-situ FIGAERO-CIMS is used (quantified using a calibration series of levoglucosan).

Figure SI 4 :
Figure SI 4: Average potential emission sensitivity fields displaying the typical residence times of air masses over different regions prior to their arrival at Beijing during the three different time periods.A clear transition from predominantly northwesterly and local air masses in winter to a more southeasterly influence during summer is observed due to the East Asian monsoon cycle.

Figure SI 5
Figure SI 5: 3-day backward dispersion maps of air observed at the Beijing site in contact with the surface, colored by eBC -used as a pollution transport marker -concentration observed at the site.The average for the three different time periods is shown.High values over specific areas suggest strong emissions in these areas (or within the typical transport route passing over this area).The center of the colorbar shows the mean value of sulfate over the whole observation period and the axis extends to 5 times the mean and 0.2 times the mean in logarithmic scale.

Figure SI 6 :
Figure SI 6: Average age of air mass arriving to Beijing for different time periods: Average time air masses take to arrive to the receptor site in Beijing after leaving a specific grid cell.

Figure
Figure SI 9: 3-day backward dispersion maps of air observed at the Beijing site in contact with the surface, colored by bulk PM2.5 constituent concentration observed at the site.The average for the whole measurement period is shown.High values over specific areas suggest strong precursor emissions in these areas (or within the typical transport route passing over this area).The middle of the colorbar shows the mean value of the compound over the whole observation period and the axis extends to 5 times the mean and 0.2 times the mean in logarithmic scale.

Figure SI 10 :Figure
Figure SI 10: Comparison between chemically resolved PM2.5 (non-refractory PM + eBC) and bulk PM2.5 (from surrounding stations, error bars represent the standard deviation in concentration between the surrounding stations).

Figure SI 12 :
Figure SI 12: Ratio of a contaminant's signal ([ −]   ) integrated over the entire thermogram during the first compared to the second desorption cycle as a function of the total signal ([ −]   ) from ions other than the reagent ion.

Figure SI 14 :
Figure SI 14: Blank parametrization for two example peaks (C6H5NO3I -, C6H10O5I -) as Iblank/Iambient as a function of the filter loading (Iambient*Vair) as well as the cumulative distribution function (CDF) of ambient measurements.

Figure SI 15 :
Figure SI 15: Change in time-dependent residual-to-measurement uncertainty ratios as a function of the number of factors).

Figure SI 16 :Figure SI 17 :
Figure SI 16: Ratio of daily mean concentrations of COA to HOA as a function of HOA (assuming RIEs of 1.4).

Figure SI 20 :
Figure SI 20: Comparison between selected OA components to markers (daily mean concentrations).

Figure
Figure SI 26: a) Particle-phase MBTCA and pinic acid seasonality, b) comparison of particle-phase MBTCA and pinic acid concentrations including days with a daily average temperature up to 20 C. At higher temperatures the the correlation deteriorates because of partitioning: Pinic acid preferentially resides in the gas-phase while MBTCA is less affected.